AI Journey

Increase transparency internally and externally, automate processes, and empower employees for informed decision-making.

AI and machine learning in data strategy

In the present day, we have unquestionably stepped into the era of AI, with artificial intelligence being a prevalent topic and an essential aspect of our daily lives. Many companies are eager to undertake AI projects, whether it be generative AI, Natural Language Processing (NLP), Large Language Models (LLM), etc. Nevertheless, the hurdles of AI typically stem from the data foundation and a somewhat technical comprehension of artificial intelligence. Data, the cornerstone of AI, must be of high quality, reliable, regular, and accurate to successfully transform it into tangible business value through AI.

Picture yourself wanting to construct your residence with an exquisite penthouse on the fourth floor, adorned with various innovations and featuring a stunning view of the lake. What would be essential for this endeavor? Three well-founded floors that are stable, reliable, and sustainably constructed, ensuring that the investment in your penthouse is worthwhile and certain innovations are possible. Applying this analogy to AI in businesses frequently reveals a distinct reality. Many companies would like to start with the penthouse, but often the lower three floors are not yet finished or are in the construction phase. The staircase to the penthouse is missing, and the conduits for the power cables have not been laid. Constructing your rooftop terrace at this moment would likely result in a temporarily aesthetically pleasing and functional space, particularly with the implementation of some workarounds to ensure its self-sufficiency. It’s just a matter of time before fundamental flaws become apparent.

Why does this situation occur frequently? Expectation management and effective marketing from the key players in the data and AI field contribute to it. Additionally, it is often the case that only the tip of the iceberg is visible.  It is vital to ponder and evaluate the extent of resources allocated by major players in the AI and data domain, and subsequently, to contextualize it within one’s own situation.

We are fully convinced that every company can benefit from AI and data. Companies simply need to be aware of what they can do and what they cannot. Is it necessary to have four floors, or is one floor sufficient in the initial phase? Expectation management, Value, and resource allocation are the central keywords in this context.

Possible concrete AI use cases for Supply Chain and Logistics include:

    • Individual demand forecasting based on specific requirements
    • Automated supplier evaluation and selection according to the supplier portfolio’s needs
    • fraud detection in deliveries and returns
    • generative AI for written offer creation or response to complaints
    • search engine using internal company data (LLM)
    • fully automated returns management with custom-trained AI models
    • simulations of the impact of geopolitical events or natural disasters on your individual supply chain
    • automated inventory optimization based on individual products in stock
    • route optimizations using AI by simulating the most efficient routes considering weather data, traffic conditions, costs, etc.
    • predictions of machinery maintenance needs through predictive maintenance
    • automated contract summarization.

    Business Value of using AI and data:

      • Create transparency and thereby improve interactions internally and externally.
      • They optimize and automate processes for more efficient and resource-efficient operations.
      • Empower employees to have the relevant information at the right time for informed decision-making.
      • Contribute to product development through additional features that differentiate them from the competition.

      Log-hub AI Journey:

        • AI Understanding and First Touch with AI: Ensure a shared understanding of AI, making it tangible and providing hands-on experience.
        • AI Use-Case Brainstorming: Identify use cases aligned with existing business challenges in Supply Chain and Logistics processes to deliver business value.
        • AI Start Developing: Begin development of the defined AI use case (Minimum Viable Product – MVP).
        • Productive AI: Finalize the AI use case for the entire organization with a service level agreement and establish a capable organization to manage the new innovation.

        Contact us

        Sandro Breandle, Chief data and analytics officer in Log-hub AG

        Chief Data and Analytics Officer

        Sandro Brändle

        Have a question about our Data, Analytics & AI Consulting Services? You can contact our CDAO directly.

        Schwandweg 5, Schindellegi

        Switzerland 8834

        Tel: +381 60 358 52 83


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